{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('../py')\n",
    "import db\n",
    "import weighted\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_original=pd.read_sql(sql=\"select * from _201903v2 where monthly_salary>0 and monthly_salary<80000 and YEAR(publish_date)=2019 and MONTH(publish_date)=3\", con=db.get_conn())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46808, 90)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_original.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_job_ids=['104660258','104142922','108434795','101357291','106253516','110368302','111391233','108665401','109277048'\n",
    "                  ,'73857191','108584955','102824950','102824949','111391233','110884556']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data_original[~data_original.job_id.isin(error_job_ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46808, 90)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['publish_date']\n",
    "del data['published_on_weekend']\n",
    "del data['title']\n",
    "del data['company_title']\n",
    "del data['company_description']\n",
    "del data['job_description']\n",
    "del data['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sub_stats_by_prefix(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    count=[]\n",
    "    \n",
    "    features_out=[]\n",
    "    for feature in features:\n",
    "        #print(feature)\n",
    "        idata=data[data[feature]==1]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(weighted.weighted_mean(values, weights))\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        features_out.append(feature)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data['rank']=range(0,len(features_out))\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "    sub_data['rank']=range(1,len(features_out)+1)\n",
    "    #sub_data=sub_data.reset_index()\n",
    "    return sub_data\n",
    "\n",
    "def apply_style(sub_data):\n",
    "    return sub_data.style.hide_index().format(\n",
    "        {\"percentage\":\"{:.2%}\",\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"salary_95_min\":\"{:.0f}\",\"salary_95_max\":\"{:.0f}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'pl_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_faf12c0c_69d4_11e9_a182_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"col_heading level0 col0\" >rank</th> \n",
       "        <th class=\"col_heading level0 col1\" >pl_</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col3\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col4\" >salary_95_min</th> \n",
       "        <th class=\"col_heading level0 col5\" >salary_95_max</th> \n",
       "        <th class=\"col_heading level0 col6\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col7\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow0_col1\" class=\"data row0 col1\" >haskell</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow0_col2\" class=\"data row0 col2\" >27772</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow0_col3\" class=\"data row0 col3\" >30667</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow0_col4\" class=\"data row0 col4\" >7544</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow0_col5\" class=\"data row0 col5\" >45000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow0_col6\" class=\"data row0 col6\" >41</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow0_col7\" class=\"data row0 col7\" >0.02%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow1_col1\" class=\"data row1 col1\" >julia</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow1_col2\" class=\"data row1 col2\" >26875</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow1_col3\" class=\"data row1 col3\" >26875</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow1_col4\" class=\"data row1 col4\" >17500</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow1_col5\" class=\"data row1 col5\" >30000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow1_col6\" class=\"data row1 col6\" >4</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow1_col7\" class=\"data row1 col7\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow2_col1\" class=\"data row2 col1\" >rust</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow2_col2\" class=\"data row2 col2\" >19567</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow2_col3\" class=\"data row2 col3\" >17000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow2_col4\" class=\"data row2 col4\" >6500</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow2_col5\" class=\"data row2 col5\" >45833</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow2_col6\" class=\"data row2 col6\" >242</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow2_col7\" class=\"data row2 col7\" >0.11%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow3_col1\" class=\"data row3 col1\" >python</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow3_col2\" class=\"data row3 col2\" >18315</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow3_col3\" class=\"data row3 col3\" >16000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow3_col4\" class=\"data row3 col4\" >4000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow3_col5\" class=\"data row3 col5\" >42500</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow3_col6\" class=\"data row3 col6\" >17854</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow3_col7\" class=\"data row3 col7\" >8.04%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow4_col1\" class=\"data row4 col1\" >matlab</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow4_col2\" class=\"data row4 col2\" >18285</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow4_col3\" class=\"data row4 col3\" >17500</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow4_col4\" class=\"data row4 col4\" >5000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow4_col5\" class=\"data row4 col5\" >40000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow4_col6\" class=\"data row4 col6\" >3304</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow4_col7\" class=\"data row4 col7\" >1.49%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow5_col1\" class=\"data row5 col1\" >perl</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow5_col2\" class=\"data row5 col2\" >17845</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow5_col3\" class=\"data row5 col3\" >15000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow5_col4\" class=\"data row5 col4\" >3750</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow5_col5\" class=\"data row5 col5\" >40000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow5_col6\" class=\"data row5 col6\" >1681</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow5_col7\" class=\"data row5 col7\" >0.76%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow6_col1\" class=\"data row6 col1\" >go</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow6_col2\" class=\"data row6 col2\" >17688</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow6_col3\" class=\"data row6 col3\" >15000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow6_col4\" class=\"data row6 col4\" >6000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow6_col5\" class=\"data row6 col5\" >40000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow6_col6\" class=\"data row6 col6\" >15426</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow6_col7\" class=\"data row6 col7\" >6.95%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow7_col1\" class=\"data row7 col1\" >ruby</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow7_col2\" class=\"data row7 col2\" >16402</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow7_col3\" class=\"data row7 col3\" >16000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow7_col4\" class=\"data row7 col4\" >3755</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow7_col5\" class=\"data row7 col5\" >31323</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow7_col6\" class=\"data row7 col6\" >733</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow7_col7\" class=\"data row7 col7\" >0.33%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow8_col1\" class=\"data row8 col1\" >lua</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow8_col2\" class=\"data row8 col2\" >16259</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow8_col3\" class=\"data row8 col3\" >15000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow8_col4\" class=\"data row8 col4\" >5250</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow8_col5\" class=\"data row8 col5\" >35000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow8_col6\" class=\"data row8 col6\" >2465</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow8_col7\" class=\"data row8 col7\" >1.11%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow9_col1\" class=\"data row9 col1\" >cpp</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow9_col2\" class=\"data row9 col2\" >15957</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow9_col3\" class=\"data row9 col3\" >13000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow9_col4\" class=\"data row9 col4\" >4000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow9_col5\" class=\"data row9 col5\" >37500</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow9_col6\" class=\"data row9 col6\" >35622</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow9_col7\" class=\"data row9 col7\" >16.04%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow10_col1\" class=\"data row10 col1\" >typescript</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow10_col2\" class=\"data row10 col2\" >14758</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow10_col3\" class=\"data row10 col3\" >13750</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow10_col4\" class=\"data row10 col4\" >6000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow10_col5\" class=\"data row10 col5\" >30000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow10_col6\" class=\"data row10 col6\" >610</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow10_col7\" class=\"data row10 col7\" >0.27%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow11_col1\" class=\"data row11 col1\" >kotlin</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow11_col2\" class=\"data row11 col2\" >14721</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow11_col3\" class=\"data row11 col3\" >12500</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow11_col4\" class=\"data row11 col4\" >6102</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow11_col5\" class=\"data row11 col5\" >30495</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow11_col6\" class=\"data row11 col6\" >501</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow11_col7\" class=\"data row11 col7\" >0.23%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow12_col1\" class=\"data row12 col1\" >swift</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow12_col2\" class=\"data row12 col2\" >14127</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow12_col3\" class=\"data row12 col3\" >12500</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow12_col4\" class=\"data row12 col4\" >5259</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow12_col5\" class=\"data row12 col5\" >30000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow12_col6\" class=\"data row12 col6\" >1645</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow12_col7\" class=\"data row12 col7\" >0.74%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow13_col1\" class=\"data row13 col1\" >java</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow13_col2\" class=\"data row13 col2\" >13836</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow13_col3\" class=\"data row13 col3\" >12500</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow13_col4\" class=\"data row13 col4\" >3750</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow13_col5\" class=\"data row13 col5\" >32500</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow13_col6\" class=\"data row13 col6\" >73081</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow13_col7\" class=\"data row13 col7\" >32.90%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow14_col1\" class=\"data row14 col1\" >objective_c</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow14_col2\" class=\"data row14 col2\" >13498</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow14_col3\" class=\"data row14 col3\" >12500</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow14_col4\" class=\"data row14 col4\" >5250</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow14_col5\" class=\"data row14 col5\" >23750</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow14_col6\" class=\"data row14 col6\" >265</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow14_col7\" class=\"data row14 col7\" >0.12%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow15_col1\" class=\"data row15 col1\" >php</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow15_col2\" class=\"data row15 col2\" >12683</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow15_col3\" class=\"data row15 col3\" >11500</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow15_col4\" class=\"data row15 col4\" >3750</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow15_col5\" class=\"data row15 col5\" >31525</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow15_col6\" class=\"data row15 col6\" >10630</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow15_col7\" class=\"data row15 col7\" >4.79%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow16_col1\" class=\"data row16 col1\" >javascript</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow16_col2\" class=\"data row16 col2\" >12112</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow16_col3\" class=\"data row16 col3\" >11500</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow16_col4\" class=\"data row16 col4\" >4000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow16_col5\" class=\"data row16 col5\" >25000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow16_col6\" class=\"data row16 col6\" >29523</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow16_col7\" class=\"data row16 col7\" >13.29%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow17_col1\" class=\"data row17 col1\" >c_sharp</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow17_col2\" class=\"data row17 col2\" >11628</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow17_col3\" class=\"data row17 col3\" >11000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow17_col4\" class=\"data row17 col4\" >3750</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow17_col5\" class=\"data row17 col5\" >24000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow17_col6\" class=\"data row17 col6\" >27370</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow17_col7\" class=\"data row17 col7\" >12.32%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow18_col1\" class=\"data row18 col1\" >vba</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow18_col2\" class=\"data row18 col2\" >11015</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow18_col3\" class=\"data row18 col3\" >11500</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow18_col4\" class=\"data row18 col4\" >2500</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow18_col5\" class=\"data row18 col5\" >20000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow18_col6\" class=\"data row18 col6\" >237</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow18_col7\" class=\"data row18 col7\" >0.11%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow19_col1\" class=\"data row19 col1\" >delphi</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow19_col2\" class=\"data row19 col2\" >9908</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow19_col3\" class=\"data row19 col3\" >9000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow19_col4\" class=\"data row19 col4\" >4552</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow19_col5\" class=\"data row19 col5\" >19310</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow19_col6\" class=\"data row19 col6\" >849</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow19_col7\" class=\"data row19 col7\" >0.38%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow20_col1\" class=\"data row20 col1\" >visual_basic</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow20_col2\" class=\"data row20 col2\" >9474</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow20_col3\" class=\"data row20 col3\" >9000</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow20_col4\" class=\"data row20 col4\" >6419</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow20_col5\" class=\"data row20 col5\" >16375</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow20_col6\" class=\"data row20 col6\" >29</td> \n",
       "        <td id=\"T_faf12c0c_69d4_11e9_a182_701ce71031efrow20_col7\" class=\"data row20 col7\" >0.01%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x192e1c6b908>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >pl_</th> \n",
       "        <th class=\"col_heading level0 col1\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >5</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow0_col0\" class=\"data row0 col0\" >java</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow0_col1\" class=\"data row0 col1\" >32.90%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow1_col0\" class=\"data row1 col0\" >cpp</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow1_col1\" class=\"data row1 col1\" >16.04%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >6</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow2_col0\" class=\"data row2 col0\" >javascript</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow2_col1\" class=\"data row2 col1\" >13.29%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >0</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow3_col0\" class=\"data row3 col0\" >c_sharp</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow3_col1\" class=\"data row3 col1\" >12.32%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >14</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow4_col0\" class=\"data row4 col0\" >python</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow4_col1\" class=\"data row4 col1\" >8.04%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >3</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow5_col0\" class=\"data row5 col0\" >go</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow5_col1\" class=\"data row5 col1\" >6.95%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >13</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow6_col0\" class=\"data row6 col0\" >php</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow6_col1\" class=\"data row6 col1\" >4.79%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row7\" class=\"row_heading level0 row7\" >10</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow7_col0\" class=\"data row7 col0\" >matlab</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow7_col1\" class=\"data row7 col1\" >1.49%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row8\" class=\"row_heading level0 row8\" >9</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow8_col0\" class=\"data row8 col0\" >lua</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow8_col1\" class=\"data row8 col1\" >1.11%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row9\" class=\"row_heading level0 row9\" >12</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow9_col0\" class=\"data row9 col0\" >perl</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow9_col1\" class=\"data row9 col1\" >0.76%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row10\" class=\"row_heading level0 row10\" >17</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow10_col0\" class=\"data row10 col0\" >swift</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow10_col1\" class=\"data row10 col1\" >0.74%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row11\" class=\"row_heading level0 row11\" >2</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow11_col0\" class=\"data row11 col0\" >delphi</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow11_col1\" class=\"data row11 col1\" >0.38%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row12\" class=\"row_heading level0 row12\" >15</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow12_col0\" class=\"data row12 col0\" >ruby</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow12_col1\" class=\"data row12 col1\" >0.33%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row13\" class=\"row_heading level0 row13\" >18</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow13_col0\" class=\"data row13 col0\" >typescript</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow13_col1\" class=\"data row13 col1\" >0.27%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row14\" class=\"row_heading level0 row14\" >8</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow14_col0\" class=\"data row14 col0\" >kotlin</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow14_col1\" class=\"data row14 col1\" >0.23%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row15\" class=\"row_heading level0 row15\" >11</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow15_col0\" class=\"data row15 col0\" >objective_c</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow15_col1\" class=\"data row15 col1\" >0.12%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row16\" class=\"row_heading level0 row16\" >16</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow16_col0\" class=\"data row16 col0\" >rust</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow16_col1\" class=\"data row16 col1\" >0.11%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row17\" class=\"row_heading level0 row17\" >19</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow17_col0\" class=\"data row17 col0\" >vba</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow17_col1\" class=\"data row17 col1\" >0.11%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row18\" class=\"row_heading level0 row18\" >4</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow18_col0\" class=\"data row18 col0\" >haskell</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow18_col1\" class=\"data row18 col1\" >0.02%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row19\" class=\"row_heading level0 row19\" >20</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow19_col0\" class=\"data row19 col0\" >visual_basic</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow19_col1\" class=\"data row19 col1\" >0.01%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031eflevel0_row20\" class=\"row_heading level0 row20\" >7</th> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow20_col0\" class=\"data row20 col0\" >julia</td> \n",
       "        <td id=\"T_fb283cf4_69d4_11e9_80c2_701ce71031efrow20_col1\" class=\"data row20 col1\" >0.00%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x192e1dbfd68>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=data_pl[['pl_','percentage']].sort_values(by='percentage', ascending=False)\n",
    "#data_pl.style.format({\"percentage\":\"{:.2%}\"})\n",
    "data_pl.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_pl=data_pl.sort_values(by='percentage')\n",
    "\n",
    "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
    "labels = data_pl['pl_']\n",
    "sizes = data_pl['percentage']\n",
    "#explode = (0, 0.1, 0, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(15, 9))\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.title(\"Programming Languages in China, March 2019\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc=WordCloud()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in data_pl.iterrows():\n",
    "    wc_dict[row['pl_']]=row['percentage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc=wc.generate_from_frequencies(wc_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.imshow(wc, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
